# 练习1：图像矩阵操作
import numpy as np

image = np.array([
    [100, 150, 200],
    [80,  120, 180],
    [60,  90,  140]
], dtype=np.uint8)

print("原图：")
print(image)

image_dark = image - 20
print("\n变暗20后的图像:")
print(image_dark)

image_crop = image[0:2,0:2]
print("\n裁剪左上角后的图像:")
print(image_crop)

image_flip = np.fliplr(image)
print("\n水平翻转后的图像:")
print(image_flip)



# 练习2：看图写代码
import numpy as np

img = np.array([
    [255, 255, 0,   0  ],
    [255, 255, 0,   0  ],
    [0,   0,   255, 255],
    [0,   0,   255, 255]
], dtype=np.uint8)

print("原始图像:")
print(img)

white_pixel_count = np.sum(img == 255)
black_pixel_count = np.sum(img == 0)
print(f"\n白色像素(255)的数量:{white_pixel_count}")
print(f"黑色像素(0)的数量:{black_pixel_count}")

flipped_img_horizontal = img[:,::-1]
print("\n水平翻转后的图像:")
print(flipped_img_horizontal)

rotated_img_ccw = img.T[:,::-1]
print("\n逆时针旋转90度后的图像:")
print(rotated_img_ccw)




# 练习3：特征向量计算
import numpy as np

# 假设这是从图像中提取的2个特征图
feature_map1 = np.array([[1, 0, 1], [0, 1, 0], [1, 0, 1]])
feature_map2 = np.array([[1, 1, 1], [1, 0, 0], [1, 0, 0]])

# 补全代码：将特征图展平为向量
vector1 = feature_map1.flatten()  # 展平
vector2 = feature_map2.flatten()  # 展平

print("vector1:", vector1)
print("vector2:", vector2)

euclidean_distance = np.linalg.norm(vector1 - vector2)
print("\n欧几里得距离:",euclidean_distance)

cosine_similarity = np.dot(vector1,vector2) / (np.linalg.norm(vector1) * np.linalg.norm(vector2))
print("余弦相似度:",cosine_similarity)